A major export of paleobotany is climate reconstructions. The ‘grand-daddy' of all leaf-climate methods is leaf-margin analysis, which is based on the positive correlation between mean annual temperature (MAT) and the fraction of untoothed woody dicot species. Another important method is leaf-area analysis, which relies on the relationship between leaf area and mean annual precipitation (MAP). Although these two methods have been largely successful for reconstructing MAT and MAP from fossil plants, a potential drawback is that each is based on only one aspect of leaf physiognomy. To address this shortcoming, several multivariate techniques have been developed, most notably CLAMP, which describes leaf physiognomy with ~30 categorical variables. However, CLAMP typically does not outperform the univariate approaches, probably because some of its variables are difficult to score in a reproducible manner. In recent years, we and other colleagues have developed a multivariate technique for reconstructing climate called digital leaf physiognomy. This method is based on characters that can be scored in a reproducible manner, such as tooth number, tooth area, and perimeter-to-area relationships. Further, digital leaf physiognomy is based mostly on continuous (not categorical) variables, and thus can better differentiate the spectrum of leaf physiognomic information. An earlier study calibrated the new method with 16 U.S. east coast floras and one Panamanian flora. Results from this study were promising: multivariate models for estimating MAT were more accurate than leaf-margin analysis, and the method could be applied to fossils. Here, we expand the present calibration to over 40 sites, including floras with highly differing phylogenetic histories and climate (including MAP). Application to four early Paleogene floras (Republic and Bonanza from western North America, and Laguna del Hunco and Salamanca from Argentina) yields estimates of MAT and MAP that are similar to estimates from more traditional proxies, but with considerably less error.